mfa的matlab源码.rar

% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol); % % Maximum Likelihood Mixture of Factor Analysis using EM % % X - data matrix % M - number of mixtures (default 1) % K - number of factors in each mixture (default 2) % cyc - maximum number of cycles of EM (default 100) % tol - termination tolerance (prop change in likelihood) (default 0.0001) % % Lh - factor loadings % Ph - diagonal uniquenesses matrix % Mu - mean vectors % Pi - priors % LL - log likelihood curve % % Iterates until a proportional change < tol in the log likelihood % or cyc steps of EM function [Lh, Ph, Mu, Pi, LL] = mfa(X,M,K,cyc,tol) if nargin<5 tol=0.0001; end; if nargin<4 cyc=100; end; if nargin<3 K=2; end; if nargin<2 M=1; end; N=length(X(:,1)); D=length(X(1,:)); tiny=exp(-700); % randn('seed',2); % XXX % rand('seed',2); fprintf('\n'); if (M==1) [Lh,Ph,LL]=ffa(X,K,cyc,tol); Mu=mean(X); Pi=1; else mX=mean(X); cX=cov(X); scale=det(cX)^(1/D); Lh=randn(D*M,K)*sqrt(scale/K); Ph=diag(cX)+tiny; Pi=ones(M,1)/M; Mu=randn(M,D)*sqrtm(cX)+ones(M,1)*mX; oldMu=Mu; I=eye(K); lik=0; LL=[]; H=zeros(N,M); % E(w|x) EZ=zeros(N*M,K); EZZ=zeros(K*M,K); XX=zeros(D*M,D); s=zeros(M,1); const=(2*pi)^(-D/2); %%%%%%%%%%%%%%%%%%%% for i=1:cyc; %%%% E Step %%%% Phi=1./Ph; Phid=diag(Phi); for k=1:M Lht=Lh((k-1)*D+1:k*D,:); LP=Phid*Lht; MM=Phid-LP*inv(I+Lht'*LP)*LP'; dM=sqrt(det(MM)); Xk=(X-ones(N,1)*Mu(k,:)); XM=Xk*MM; H(:,k)=const*Pi(k)*dM*exp(-0.5*rsum(XM.*Xk)); EZ((k-1)*N+1:k*N,:)=XM*Lht; end; Hsum=rsum(H); oldlik=lik; lik=sum(log(Hsum+(Hsum==0)*exp(-744))); Hzero=(Hsum==0); Nz=sum(Hzero); H(Hzero,:)=tiny*ones(Nz,M)/M; Hsum(Hzero)=tiny*ones(Nz,1); H=rdiv(H,Hsum); s=csum(H); s=s+(s==0)*tiny; s2=sum(s)+tiny; for k=1:M kD=(k-1)*D+1:k*D; Lht=Lh(kD,:); LP=Phid*Lht; MM=Phid-LP*inv(I+Lht'*LP)*LP'; Xk=(X-ones(N,1)*Mu(k,:)); XX(kD,:)=rprod(Xk,H(:,k))'*Xk/s(k); beta=Lht'*MM; EZZ((k-1)*K+1:k*K,:)=I-beta*Lht +beta*XX(kD,:)*beta'; end; %%%% log likelihood %%%% LL=[LL lik]; fprintf('cycle %g \tlog likelihood %g ',i,lik); if (i<=2) likbase=lik; elseif (lik<oldlik) fprintf(' violation'); elseif ((lik-likbase)<(1 + tol)*(oldlik-likbase)|~finite(lik)) break; end; fprintf('\n'); %%%% M Step %%%% % means and covariance structure Ph=zeros(D,1); for k=1:M kD=(k-1)*D+1:k*D; kK=(k-1)*K+1:k*K; kN=(k-1)*N+1:k*N; T0=rprod(X,H(:,k)); T1=T0'*[EZ(kN,:) ones(N,1)]; XH=EZ(kN,:)'*H(:,k); T2=inv([s(k)*EZZ(kK,:) XH; XH' s(k)]); T3=T1*T2; Lh(kD,:)=T3(:,1:K); Mu(k,:)=T3(:,K+1)'; T4=diag(T0'*X-T3*T1')/s2; Ph=Ph+T4.*(T4>0); end; Phmin=exp(-700); Ph=Ph.*(Ph>Phmin)+(Ph<=Phmin)*Phmin; % to avoid zero variances % priors Pi=s'/s2; end; fprintf('\n'); end;